Multivariate analysis to research innovation complementarities

Autores
Morero, Hernan; Ortiz, Pablo
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
It is widely recognized that orthodox economics is obsessed with econometrics tools. However, econometrics techniques have a limited capacity to deal with qualitative variables coming from surveys. This paper presents a defence of the use of statistical methods, in particular multivariate analysis, which is the overall objective of the paper. Multivariate analysis is a set of methods that can be used when the problem that arises implies multiple dependent or interdependent variables of a qualitative nature. We considered an issue in the literature to probe multivariate analysis in a particular topic, namely: the question of innovation complementarities. We analyzed the presence of complementarities between internal and external innovation activities in 257 software firms from Argentina during the period 2008–2010, comparing the consideration of the problem of complementarities with the more modern complementarity econometrical tests, super and sub modularity tests arising from diverse firm-innovation function estimations (OProbit, Tobit and Probit), with the engagement of the same issue with multiple factor analysis and cluster techniques. The results show not only that the same results obtained by the econometrical tools can be reached by multivariate analysis techniques, but also that multiple factor analysis and cluster techniques allow for better exploitation of the richness of qualitative data.
Fil: Morero, Hernan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigaciones y Estudios sobre Cultura y Sociedad. Universidad Nacional de Córdoba. Centro de Investigaciones y Estudios sobre Cultura y Sociedad; Argentina
Fil: Ortiz, Pablo. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas. Departamento de Economía; Argentina
Materia
Innovation Complementarities
Multivariate Analysis
Plurality
Software Sector
Supermodularity
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/58286
Acceder
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spelling Multivariate analysis to research innovation complementaritiesMorero, HernanOrtiz, PabloInnovation ComplementaritiesMultivariate AnalysisPluralitySoftware SectorSupermodularityhttps://purl.org/becyt/ford/5.2https://purl.org/becyt/ford/5It is widely recognized that orthodox economics is obsessed with econometrics tools. However, econometrics techniques have a limited capacity to deal with qualitative variables coming from surveys. This paper presents a defence of the use of statistical methods, in particular multivariate analysis, which is the overall objective of the paper. Multivariate analysis is a set of methods that can be used when the problem that arises implies multiple dependent or interdependent variables of a qualitative nature. We considered an issue in the literature to probe multivariate analysis in a particular topic, namely: the question of innovation complementarities. We analyzed the presence of complementarities between internal and external innovation activities in 257 software firms from Argentina during the period 2008–2010, comparing the consideration of the problem of complementarities with the more modern complementarity econometrical tests, super and sub modularity tests arising from diverse firm-innovation function estimations (OProbit, Tobit and Probit), with the engagement of the same issue with multiple factor analysis and cluster techniques. The results show not only that the same results obtained by the econometrical tools can be reached by multivariate analysis techniques, but also that multiple factor analysis and cluster techniques allow for better exploitation of the richness of qualitative data.Fil: Morero, Hernan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigaciones y Estudios sobre Cultura y Sociedad. Universidad Nacional de Córdoba. Centro de Investigaciones y Estudios sobre Cultura y Sociedad; ArgentinaFil: Ortiz, Pablo. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas. Departamento de Economía; ArgentinaTaylor & Francis2017-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/58286Morero, Hernan; Ortiz, Pablo; Multivariate analysis to research innovation complementarities; Taylor & Francis; African Journal of Science, Technology, Innovation and Development; 10-2017; 1-162042-13382042-1346CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.tandfonline.com/doi/full/10.1080/20421338.2017.1355586info:eu-repo/semantics/altIdentifier/doi/10.1080/20421338.2017.1355586info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:10:10Zoai:ri.conicet.gov.ar:11336/58286instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-10 13:10:10.407CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Multivariate analysis to research innovation complementarities
title Multivariate analysis to research innovation complementarities
spellingShingle Multivariate analysis to research innovation complementarities
Morero, Hernan
Innovation Complementarities
Multivariate Analysis
Plurality
Software Sector
Supermodularity
title_short Multivariate analysis to research innovation complementarities
title_full Multivariate analysis to research innovation complementarities
title_fullStr Multivariate analysis to research innovation complementarities
title_full_unstemmed Multivariate analysis to research innovation complementarities
title_sort Multivariate analysis to research innovation complementarities
dc.creator.none.fl_str_mv Morero, Hernan
Ortiz, Pablo
author Morero, Hernan
author_facet Morero, Hernan
Ortiz, Pablo
author_role author
author2 Ortiz, Pablo
author2_role author
dc.subject.none.fl_str_mv Innovation Complementarities
Multivariate Analysis
Plurality
Software Sector
Supermodularity
topic Innovation Complementarities
Multivariate Analysis
Plurality
Software Sector
Supermodularity
purl_subject.fl_str_mv https://purl.org/becyt/ford/5.2
https://purl.org/becyt/ford/5
dc.description.none.fl_txt_mv It is widely recognized that orthodox economics is obsessed with econometrics tools. However, econometrics techniques have a limited capacity to deal with qualitative variables coming from surveys. This paper presents a defence of the use of statistical methods, in particular multivariate analysis, which is the overall objective of the paper. Multivariate analysis is a set of methods that can be used when the problem that arises implies multiple dependent or interdependent variables of a qualitative nature. We considered an issue in the literature to probe multivariate analysis in a particular topic, namely: the question of innovation complementarities. We analyzed the presence of complementarities between internal and external innovation activities in 257 software firms from Argentina during the period 2008–2010, comparing the consideration of the problem of complementarities with the more modern complementarity econometrical tests, super and sub modularity tests arising from diverse firm-innovation function estimations (OProbit, Tobit and Probit), with the engagement of the same issue with multiple factor analysis and cluster techniques. The results show not only that the same results obtained by the econometrical tools can be reached by multivariate analysis techniques, but also that multiple factor analysis and cluster techniques allow for better exploitation of the richness of qualitative data.
Fil: Morero, Hernan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigaciones y Estudios sobre Cultura y Sociedad. Universidad Nacional de Córdoba. Centro de Investigaciones y Estudios sobre Cultura y Sociedad; Argentina
Fil: Ortiz, Pablo. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas. Departamento de Economía; Argentina
description It is widely recognized that orthodox economics is obsessed with econometrics tools. However, econometrics techniques have a limited capacity to deal with qualitative variables coming from surveys. This paper presents a defence of the use of statistical methods, in particular multivariate analysis, which is the overall objective of the paper. Multivariate analysis is a set of methods that can be used when the problem that arises implies multiple dependent or interdependent variables of a qualitative nature. We considered an issue in the literature to probe multivariate analysis in a particular topic, namely: the question of innovation complementarities. We analyzed the presence of complementarities between internal and external innovation activities in 257 software firms from Argentina during the period 2008–2010, comparing the consideration of the problem of complementarities with the more modern complementarity econometrical tests, super and sub modularity tests arising from diverse firm-innovation function estimations (OProbit, Tobit and Probit), with the engagement of the same issue with multiple factor analysis and cluster techniques. The results show not only that the same results obtained by the econometrical tools can be reached by multivariate analysis techniques, but also that multiple factor analysis and cluster techniques allow for better exploitation of the richness of qualitative data.
publishDate 2017
dc.date.none.fl_str_mv 2017-10
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/58286
Morero, Hernan; Ortiz, Pablo; Multivariate analysis to research innovation complementarities; Taylor & Francis; African Journal of Science, Technology, Innovation and Development; 10-2017; 1-16
2042-1338
2042-1346
CONICET Digital
CONICET
url http://hdl.handle.net/11336/58286
identifier_str_mv Morero, Hernan; Ortiz, Pablo; Multivariate analysis to research innovation complementarities; Taylor & Francis; African Journal of Science, Technology, Innovation and Development; 10-2017; 1-16
2042-1338
2042-1346
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.tandfonline.com/doi/full/10.1080/20421338.2017.1355586
info:eu-repo/semantics/altIdentifier/doi/10.1080/20421338.2017.1355586
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Taylor & Francis
publisher.none.fl_str_mv Taylor & Francis
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.993085

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